Temperature control in 5th generation district heating and cooling networks: An MILP-based operation optimization. (15th April 2021)

• Record Type: Journal Article
• Authors: Wirtz, Marco; Neumaier, Lisa; Remmen, Peter; Müller, Dirk
• Is Part Of: Applied energy. Volume 288(2021)
• Publication Date: 2021-04-15
• Subjects: District heating -- District cooling -- 5GDHC -- Operational optimization -- MILP -- Network temperature; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.116608
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: In order to realize an energy efficient and emission-free heat and cold supply in urban areas, 5th Generation District Heating and Cooling (5GDHC) networks are a promising technology. In 5GDHC networks, the control of the network temperature is crucial since it affects the efficiency of connected heat pumps and chillers, the heat losses (or gains) of the network, as well as the integration of waste heat or free cooling. Due to the large number of opposing effects, the optimal control of network temperatures is a challenging task. In this paper, a mixed-integer linear program (MILP) is proposed for short-term optimization of the network temperature in 5GDHC systems. The model comprises an air-source heat pump, compression chiller and thermal storage in a central generation unit as well as heat pumps, chillers, electric boilers and thermal storages in buildings. Furthermore, the model considers the thermal inertia of the water mass in the network which functions as additional thermal storage. The optimization model is real-time capable and designed to be deployed in a model-predictive control. In a case study, the optimization approach leads to cost savings in two of three investigated months (by 10 % and 60 % respectively) compared to a reference operation strategy (free floating network temperature). Highlights: Operation optimization model for 5th generation district heating and cooling grids. Network temperature levels are discretized resulting in an MILP formulation. Performance comparison with free-floating temperature control. Investigation of storage tank and thermal inertia of network for demand balancing. Optimization model results in lower costs compared to free-floating temperature. Graphical abstract: